* Nomic Embed Text V2 with Mixture-of-Experts (MoE) architecture
- Adds MoE-based embedding model supporting multilingual embeddings.
- Selects architecture variant based on hyperparameter detection (MoE layers).
- Removes unnecessary subclass initialization checks for clarity.
https://www.nomic.ai/blog/posts/nomic-embed-text-v2
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
* fix tokenizer
* don't rename this tensor
---------
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
* fix wrong template in GLM4-0414
* fix spaces
* no bos token since it is already in the template
* moved the chatgml4 check to higher priority
* restored template for old GLM models
* moved the GLM4 template check in the correct place with correct check
* Force FP32 compute in cuBLAS GEMM
* Revert "Force FP32 compute in cuBLAS GEMM"
This reverts commit 6efd872732.
* Force F32 compute in GLM4 ffn down
* Edit comment to clarify issue
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cmake : do not include ./src as public for libllama
ggml-ci
* cmake : rework tests
ggml-ci
* llguidance : remove unicode include
ggml-ci
* cmake : make c++17 private
ggml-ci
* add pixtral text model (vision is wip)
* cgraph ok, just missing 2D RoPE
* fix bad rebase
* first working version
* fix problem with img_break token
* support dynamic image size
* update docs
* update test script
* mtmd : merge `llava-cli` and `gemma3-cli` into single `mtmd-cli`
* support for minicpmv
* remove cpp files of llava and minicpmv
* update hot topics
* mtmd : add not supported msg for qwen2vl
* Update examples/llava/mtmd.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* graph : make mla compatible with FA
* metal : add exp FA kernels for DeepSeek models
ggml-ci
* llama : minor naming updates
ggml-ci
* ggml : disable FA for DS head sizes
* tests : add FA tests for MLA shapes
ggml-ci
The Granite's FIM tokens are very similar to Qwen's; it's just that
they use underscore instead of a dash. So <fim_middle> for example
instead of <fim-middle>.
Opening up tokenizer_config.json in ibm-granite/granite-3.3-8b-base
shows:
```
"<fim_prefix>",
"<fim_middle>",
"<fim_suffix>",
"<fim_pad>",
...
"<reponame>",
```
* Merged using squash to remove all noise commit messages
* Force flash attention off for `LLM_ARCH_DEEPSEEK2` - embedding too large
* Removed 3 conts (2x RoPE and 1x RMS-norm)
* Changed to use `<cmath>` instead of `<math.h>`
* Reverted removal of the 3 conts
* Used `reshape` in `llm_graph_context::build_attn_mha()`
* Use `k_pe = ggml_reshape`
* Removed the 3 conts again
* Removed the 3D views of `wk_b` and `wv_b`, and just save and 3D in GGUF
* Removed MQA optimisation from `build_attn_mha()` as no gains now
* Simplified `is_mla` branch in `llm_build_deepseek2()`
* Removed `build_attn_mla` and added `nullptr` to all `build_atnn` calls
* Fixed call to `build_attn` in `llm_build_t5_enc`
* Add llama_model_quantize_params parameters
* Add new quantize parameters parsing and validation
* Update usage
* Add new parameters defaults
* Add new quantization parameters logic
* Add llama_model_quantize_params parameters
* Add new quantize parameters parsing and validation
* Update usage
* Add new parameters defaults
* Add new quantization parameters logic
* Minor refactoring as per the contributors' coding guidelines
* Update descriptions to match existing style
* Add llama_model_quantize_params parameters
* Add new quantize parameters parsing and validation
* Update usage
* Add new parameters defaults
* Add new quantization parameters logic
* Minor refactoring as per the contributors' guidelines
* Implement general --tensor-type instead of tensor-specific command option
* Fix implied type bug
* Restore missing #includes
* Add regex capability for tensor selection
* Refactor function name and update ALLOWED_TENSOR_TYPE
* Add missing #include
* Handle edge case when tensor name is cls.output
* Minor logging improvement
* ggml : FA supports F32 V
* graph : cast KV to F16 when the KV cache is not used
ggml-ci
* server : add test that exercises embeddings with FA enabled
ggml-ci
* model : print tensor size during load
* cont : fix units MB -> MiB
Co-authored-by: Diego Devesa <slarengh@gmail.com>
---------
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* vocab : add special infill tokens for CodeLlama
The commit adds the following special tokens for CodeLlama infill:
- `▁<PRE>`
- `▁<SUF>`
- `▁<MID>`
The motivation for this is that currently the infill example uses
CodeLlama as a suggested model. But when using this model the following
error is generated:
```console
/llama.cpp-debug/examples/infill/infill.cpp:165: GGML_ASSERT(llama_vocab_fim_pre(vocab) >= 0) failed
Could not attach to process. If your uid matches the uid of the target
process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try
again as the root user. For more details, see /etc/sysctl.d/10-ptrace.conf
ptrace: Operation not permitted.
No stack.
The program is not being run.
305251 Aborted (core dumped)
./build/bin/llama-infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf \
-c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 \
--in-prefix "def helloworld():\n print(\"hell" \
--in-suffix "\n print(\"goodbye world\")\n "
```
* squash! vocab : add special infill tokens for CodeLlama
Add _<EOT> as well.
this allow to use GPU host when possible over CPU repack.
this have the same effect to resolve this issues (#12498) without
completely disable CPU extra buffer.
Co-authored-by: philou <philou@framework>
* ggml : FA with different K, V head sizes (CPU)
ggml-ci
* metal : add FA with HS=192
* metal : extend FA to support different K and V head sizes
ggml-ci
* metal : add FA vector kernels for heads K 192 and V 128
ggml-ci
* ggml : restrict op on other backends to equal head sizes
ggml-ci
* metal : optimize FA-vec kernel
ggml-ci
* metal : FA remove mq registers
* metal : improve MoE mul_mat_id condition
ggml-ci
* metal : fix comments + remove unnecessary addition
ggml-ci
* metal : avoid too much shared memory usage with mul_mat_id
ggml-ci
* add edgellm model arch[conversation feature doesn't work]
* remove output.weight layer for edgellm arch
* [Model] update the name of the model
* update the name of model arch in convert gguf
* [Model] Refarctor the model arch into llama-model
* [Bug] Fix the bug in create attn kv
* [Code] Fix editorconfig erros
* [Code] Remove Trailing whitespace
* [Code] Remove Trailing whitespace
* [Code] Change the order of model arch in list
* [Code] Fix flake8 Lint errors
* Remove trailing white space
* [Code] Remove call in model arch
* Add support for GPT2, Bloom and CodeShell tied word embeddings
* Deduplicate tied word embeddings weights
* Workaround for incorrect weight map
It appears transformer.wte.weight is in the weight map even though the weights are not there, remove it if output weights are encountered first.
* check++
* fatfingers--
* graph : normalize Q, K, V shapes and add comments
ggml-ci
* context : synchronize before getting cross attention data
* model : fix command-r attention norm check
* llama : introduce llama_set_warmup() API call that controls warmup mode; use all MoE experts during warmup
* common : use new API to enable warmup mode during model warmup
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* sampler: turn lazy grammar trigger words to regexes
* add scripts/tool_bench.sh & .py
* constrain llama json output regardless of function name if matches at beginning
* update relaxed newline space rule in grammar tests
* support add_generation_prompt query parameter (useful for /apply_template)
* Update src/llama-grammar.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : add xcframework build script
This commit adds a script to build an XCFramework for Apple
ios, macos, visionos, and tvos platforms.
The generated XCFramework can then be added to a project and used in
the same way as a regular framework. The llama.swiftui example project
has been updated to use the XCFramework and can be started using the
following command:
```console
$ open examples/llama.swiftui/llama.swiftui.xcodeproj/
```
Refs: https://github.com/ggml-org/llama.cpp/issues/10747
* examples : remove llama.cpp (source dir ref) from project.pbxproj
This commit removes the reference to llama.cpp from the project.pbxproj
file since Package.swift has been removed.
* ci : updated build.yml to use build-xcframework.sh
* ci : add xcframework build to github releases
This commit adds the ability to create a GitHub release with the
xcframework build artifact.
* scripts : add apple app validation scripts
This commit adds scripts that can validate the iOS, macOS, tvOS, and
VisionOS applications. The scripts create a simple test app project,
copy the llama.xcframework to the test project, build and archive the
app, create an IPA from the archive, and validate the IPA using altool.
The motivation for this is to provide some basic validation and
hopefully avoid having to manually validate apps in Xcode.
* llama : remove Package.swift
This commit removes the Package.swift file, as we are now building an
XCFramework for the project.
* llama : remove Sources and spm-headers directories
* llama : use TargetConditionals.h for visionOS/tvOS
* Add include files for std::min/max and std::toupper/tolower
* win32: move _USE_MATH_DEFINES before includes to ensure M_PI is defined
* Use GGML_RESTRICT instead of "restrict" keyword everywhere, and use "__restrict" in MSVC plain C mode
* win32: only use __restrict in MSVC if C11/C17 support is not enabled
---------
Co-authored-by: Marcus Groeber <Marcus.Groeber@cerence.com>
* Added Phi-4-mini-instruct support
* Update regex per ngxson
* Change the vocab base to Xenova/gpt-4o
* fix conversion update script
* no need to check longrope
* minor style fix
* fix python style
---------
Co-authored-by: Nicholas Sparks <nisparks@microsoft.com>
It's useful to be able to have this from the library layer as it's a key
parameter of the model (e.g. to figure out how much KV cache memory is
needed).
This commit adjusts the indentation for the functions `parse_sequence`
and `parse_rule` in src/llama-grammar.cpp.
The motivation is consistency and improve readability.
* extract & return thoughts in reasoning_content field (unless --reasoning-format) for DeepSeek R1 & Command R7B
* tool-calls: add deepseek r1 template (models/templates/llama-cpp-deepseek-r1.jinja) + hackommodate broken official template
* tool-calls: accommodate variety of wrong tool call opening tags both R1 Qwen 32B and 7B distills like to spit out
* server/oai: ensure content is null when there are tool calls, and reasoning_content appears before content for readability
* tool-calls: add DeepSeek R1 Qwen distills to server/README.md & server tests
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Technically the fixed width types come only from iostream and
cstdint/stdint.h headers. memory and vector headers should not provide
these. In GCC 15 the headers are cleaned up and you require the proper
header cstdint.
src/llama-mmap.h:26:5: error: ‘uint32_t’ does not name a type
26 | uint32_t read_u32() const;
| ^~~~~~~~
Silently insert U+FFFD(s) (Unicode replacement character) instead until the
next valid codepoint can be found.
This fixes `llama_tokenize` throwing an exception across the C API boundary
or libllama's module boundary (the caller's runtime might be incompatible!)
Returing a proper error code might be desirable, however the signature
of `llama_tokenize` doesn't allow it as all return values already have
existing meaning.
* Update llama.cpp
For display progress dots in terminal.
Without this it didn't display dots progress during loading model from file.
* Update llama.cpp
removed trailing spaces
The C API in llama.h claims users can implement `llama_sampler_i` to
create custom `llama_sampler`. The sampler chain takes ownership and
calls `llama_sampler_free` on them. However, `llama_sampler_free` is
hard-coded to use `delete`. This is undefined behavior if the object
wasn't also allocated via `new` from libllama's C++ runtime. Callers
in C and C-compatible languages do not use C++'s `new` operator. C++
callers may not be sharing the same heap as libllama.
* add glm edge chat model
* use config partial_rotary_factor as rope ratio
* support for glm edge model
* vision model support
* remove debug info
* fix format
* llava.cpp trailing whitespace
* remove unused AutoTokenizer
* Update src/llama.cpp for not contain <|end|> or </s>
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* add edge template
* fix chat template
* fix confict
* fix confict
* fix ci err
* fix format err
* fix template err
* 9b hf chat support
* format
* format clip.cpp
* fix format
* Apply suggestions from code review
* Apply suggestions from code review
* Update examples/llava/clip.cpp
* fix format
* minor : style
---------
Co-authored-by: liyuhang <yuhang.li@zhipuai.cn>
Co-authored-by: piDack <pcdack@hotmail.co>
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
Co-authored-by: liyuhang <yuhang.li@aminer.cn>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* impl::load change map bpe_ranks to onordered map for reduce time of impl::load on 30%
* llama_model_loader::init_mapping - replace new llama_mmap to std::make_unique<llama_mmap> for clean code & reduce (/2) time of running init_mappings
* Update src/llama-vocab.cpp
---------
Co-authored-by: lexasub <empty@empty.ru>
Co-authored-by: Diego Devesa <slarengh@gmail.com>
This commit removes the 'd' from the log message in llama-vocab.cpp
when logging a bad special token.
The motivation for this is that currently the output can look something
like the following:
```console
load: bad special token:
'tokenizer.ggml.image_token_id' = 128256d, using default id -1
```
* (wip) support mergekit-extracted lora
* support mergekit-extract-lora
* use lora->get_scale
* correct comment
* correct norm name & condition
* add some hints
* GGUF: C++ refactor, backend support, misc fixes
remove ggml_tensor.backend
update CODEOWNERS [no ci]
remove gguf_get_data from API
revise GGUF API data types
This commit renames the `batch` parameter to `ubatch` in the
`llama_kv_cache_find_slot`, `llm_build_inp_embd`, and
`llm_build_mamba` functions.
The motivation for this is that this should have been done as part of
Commit 19d900a756 ("llama : rename batch
to ubatch (#9950)") but for some reason I missed these functions in
that commit and only noticed them now (sorry).
* convert : extend DEEPSEEK2 model architecture to support DeepseekV3ForCausalLM by adding EXPERT_WEIGHTS_NORM and EXPERT_GATING_FUNC model parameters and FFN_EXP_PROBS_B tensor type
* vocab : add DeepSeek V3 pre-tokenizer regexes
* unicode : handle ACCENT_MARK and SYMBOL categories in regex
* llama : add DeepSeek V3 chat template, handle new model parameters and tensor types
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* Add Falcon3 model support
* Add fix for adding bos to added special tokens
* Add comment explaining the logic behind the if statement
* Add a log message to better track the when the following line of code is triggered
* Update log to only print when input and output characters are different
* Fix handling pre-normalized tokens
* Refactoring
* convert : use GPT2 vocab for Phi-4 model
* convert : use null value of sliding_window to distinguish Phi-4 from other PHI3-based models
* llama : do not use sliding window attention mask for Phi-4 model
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* sampling : refactor + optimize penalties sampler
ggml-ci
* common : apply ignore_eos as logit bias
ggml-ci
* batched : remove penalties sampler
* params : allow penalty_last_n == -1 to be equal to context size
ggml-ci
* common : by default, move the penalties at the end of the sampling chain
ggml-ci
* common : ignore all EOG tokens
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* common : move back the penalties at the front of the sampling chain
ggml-ci
* readme : restore hint about --ignore-eos flag [no ci]
* llama : minor
ggml-ci
* webui : update
---------
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* Add deepseek v1 arch & gigachat template
* improve template code
* add readme
* delete comments
* remove comment
* fix format
* lint llama.cpp
* fix order of deepseek and deepseek2, move gigachat temlate to the end of func
* fix order of deepseek and deepseek2 in constants; mark shared exp as deepseek arch need
* remove comments
* move deepseek above deepseek2
* change placement of gigachat chat template
* rename ggml-cpu-aarch64.c to .cpp
* reformat extra cpu backend.
- clean Q4_0_N_M and IQ4_0_N_M
- remove from "file" tensor type
- allow only with dynamic repack
- extract cpu extra bufts and convert to C++
- hbm
- "aarch64"
- more generic use of extra buffer
- generalise extra_supports_op
- new API for "cpu-accel":
- amx
- aarch64
* clang-format
* Clean Q4_0_N_M ref
Enable restrict on C++
* add op GGML_OP_MUL_MAT_ID for Q4_0_N_M with runtime repack
* added/corrected control on tensor size for Q4 repacking.
* Update ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* add debug logs on repacks.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : add enum for supported chat templates
* use "built-in" instead of "supported"
* arg: print list of built-in templates
* fix test
* update server README
* Templates: `mistral-v1`, `mistral-v2`, `mistral-v3`, `mistral-v3-tekken`
* Changed system message logic and added tests for all 4
* Invalid `system_message` instead of `content` fixed
* Removed tab-indented lines
* Added template code and test for `mistral-v7`
* Added all tests. Fixed bug with `tmpl == "llama2"` test.
* Replaced tabs with spaces.
* Removed `'mistral-v2'` option as no (open) models ever used it
* Removed all references to 'v2' template from comments
* Update llama.cpp
Fixed `trim_assistant_message` bug
* llama : accept a list of devices to use to offload a model
* accept `--dev none` to completely disable offloading
* fix dev list with dl backends
* rename env parameter to LLAMA_ARG_DEVICE for consistency
* Add OLMo November 2024 constants
* Add OLMo November 2024 converter
* Add loading of OLMo November 2024 tensors and hyper parameters
* Add building of OLMo November 2024 model
* llama: propagating the results of `graph_compute` to the user interface
* llama: reverting kv_cache in case of failed compute
* llama: `llama_kv_cache_state` was removed, only the result of `llama_graph_compute` is returned
* llama: restore a kv_cache in case of failed computation
* llama: correct reverting of the entire batch.
also updates `llama_kv_cache_find_slot`, will correctly count the number of `used` cells for recurrent models
* llama: updated comments
* llama : add comments about KV cache state after error
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* rwkv6: rename to wkv6
* rwkv6: support avx2 avx512 armv8 armv9
* rwkv6: update cuda file name
* rwkv6: rename params
* wkv on sycl
* sycl: add some ops
* sycl: Enhance OP support judgment
* wkv6: drop armv9 and tranfer to GGML style
ggml-ci
* sync : ggml
* update the function to use appropriate types
* fix define error
* Update ggml/src/ggml-cpu.c
* add appropriate asserts
* move element-wise functions outside
* put the declaration outside the loop
* rewrite to be more inline with the common pattern for distributing threads
* use recommended way GGML_TENSOR_LOCALS
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Diego Devesa <slarengh@gmail.com>
Co-authored-by: Plamen Minev <pacominev@gmail.com>
Co-authored-by: Yuri Khrustalev <ykhrustalev@users.noreply.github.com>
Co-authored-by: Meng, Hengyu <airdldl@163.com>
* llama : fix buffer checks for mamba and rwk
* llama : fix missing worst case flag during reserve
* cuda : fix supports_op for norm
* disable sched SET_CAUSE
* Add granite template to llama.cpp
* Add granite template to test-chat-template.cpp
* Update src/llama.cpp
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* Update tests/test-chat-template.cpp
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* Added proper template and expected output
* Small change to \n
Small change to \n
* Add code space &
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* Fix spacing
* Apply suggestions from code review
* Update src/llama.cpp
---------
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
This commit renames the member field batch in llm_build_context to
ubatch, and also the parameter batch in llama_build_graph, and
llama_set_inputs to ubatch.
The motivation for this change is to make the code more readable
(considering there are the structs llama_batch and llama_sbatch), and
consistent with other parts of the code base where parameters/fields of
type llama_ubatch are named ubatch.
* [CANN] Adapt to dynamically loadable backends mechanism
* Fix the Bug: inference running result is garbled in debug running model for LM models who's type is Q4_0 class
* Handle the review comments of this pull request
* llama : deprecate softmax sampler + fix dist sampler
ggml-ci
* tests : replace macros with functions
ggml-ci
* sampling : change temperature sampler logic
For t <= 0.0f, keep the max logit intact and set the rest to -inf
* cont : no need for special "greedy" logic
top-k == 1 is the same
* tests : init prob correctly
* llama : handle temp <= 0.0 in the temp_ext sampler too
ggml-ci
* cont : avoid extra loop in temperature sampler for sub-zero temp
ggml-ci
add intel amx isa detection
add vnni kernel for gemv cases
add vnni and amx kernel support for block_q8_0
code cleanup
fix packing B issue
enable openmp
fine tune amx kernel
switch to aten parallel pattern
add error message for nested parallelism
code cleanup
add f16 support in ggml-amx
add amx kernels for QK_K quant formats: Q4_K, Q5_K, Q6_K and IQ4_XS
update CMakeList
update README
fix some compilation warning
fix compiler warning when amx is not enabled
minor change
ggml-ci
move ggml_amx_init from ggml.c to ggml-amx/mmq.cpp
ggml-ci
update CMakeLists with -mamx-tile, -mamx-int8 and -mamx-bf16
ggml-ci
add amx as an ggml-backend
update header file, the old path for immintrin.h has changed to ggml-cpu-impl.h
minor change
update CMakeLists.txt
minor change
apply weight prepacking in set_tensor method in ggml-backend
fix compile error
ggml-ci
minor change
ggml-ci
update CMakeLists.txt
ggml-ci
add march dependency
minor change
ggml-ci
change ggml_backend_buffer_is_host to return false for amx backend
ggml-ci
fix supports_op
use device reg for AMX backend
ggml-ci
minor change
ggml-ci
minor change
fix rebase
set .buffer_from_host_ptr to be false for AMX backend
* llama : suppress conversion from 'size_t' to 'int'
This commit updates llm_tokenizer_spm.tokenize to suppress/remove the
following warnings that are generated on Windows when using MSVC:
```console
src\llama-vocab.cpp(211,1): warning C4267: 'argument':
conversion from 'size_t' to 'int', possible loss of data
src\llama-vocab.cpp(517,1): warning C4267: 'argument':
conversion from 'size_t' to 'int', possible loss of data
```
This is done by adding a cast for the size_t returned from
symbols.size(). I believe this is safe as it seems unlikely that
symbols, which stores an entry for each UTF8 character, would become
larger than INT_MAX.
The motivation for this change is to reduce the number of warnings that
are currently generated when building on Windows.
* squash! llama : suppress conversion from 'size_t' to 'int'
Move cast into for loop.
* Initial XTC commit
Adds XTC sampler, not activated by default, but recommended settings by default.
* Cleanup
* Simplified chances calculation
To be more inline with the original implementation, chance is calculated once at the beginning.
* First fixes by comments
Still need to look into sorting
* Fixed trailing backspaces
* Fixed RNG to be reproduceable
Thanks to @slaren for directions
* Fixed forgotten header
* Moved `min_keep`
Moved from conditions to a simple check at the end.
* Fixed broken randomization
Thanks to @slaren for explanation
* Swapped sorting for a custom algorithm
Shifts tokens to remove the penalized ones, then puts the penalized at the back. Should make `min_keep` still viable.
* Algorithm rework
1. Scan token from top till the first non-penalizable
2. Remove the last captured token (the least probable above threshold)
3. Shift all tokens to override the remaining penalizable
4. Penalize and put them at the the bottom.
* Added XTC to `test-sampling`
* Simplified algorithm and more tests
* Updated info in common and args
* Merged back lost commits in common and arg
* Update dump info in common
* Fixed incorrect min_keep check
* Added XTC to README
* Renamed parameters, fixed info and defaults
* probability is at 0 by default, but XTC is included in sampling queue
* threshold higher than 0.5 switches XTC off
* Initial server support
* Added XTC to server UIs
* Fixed labels in old server UI
* Made algorithm safer and more readable
* Removed xtc_threshold_max
* Fixed arg after update
* Quick fixes by comments
* Simplified algorithm since threshold_max is removed
* Renamed random distribution
* Fixed tests and outdated README
* Small fixes
* server : accept extra_context for the infill endpoint
ggml-ci
* server : update readme [no ci]
* server : use repo-level FIM pattern if possible
ggml-ci
* llama : improve infill support
ggml-ci
* llama : add more FIM token strings
ggml-ci
* server : update prompt on slot restore (#9800)
* gguf : deprecate old FIM token KVs
* ggml : do not use BLAS with types without to_float
* ggml : return pointer from ggml_internal_get_type_traits to avoid unnecessary copies
* ggml : rename ggml_internal_get_type_traits -> ggml_get_type_traits
it's not really internal if everybody uses it
* ggml : add metal backend registry / device
ggml-ci
* metal : fix names [no ci]
* metal : global registry and device instances
ggml-ci
* cont : alternative initialization of global objects
ggml-ci
* llama : adapt to backend changes
ggml-ci
* fixes
* metal : fix indent
* metal : fix build when MTLGPUFamilyApple3 is not available
ggml-ci
* fix merge
* metal : avoid unnecessary singleton accesses
ggml-ci
* metal : minor fix [no ci]
* metal : g_state -> g_ggml_ctx_dev_main [no ci]
* metal : avoid reference of device context in the backend context
ggml-ci
* metal : minor [no ci]
* metal : fix maxTransferRate check
* metal : remove transfer rate stuff
---------
Co-authored-by: slaren <slarengh@gmail.com>
* rerank : use [SEP] token instead of [BOS]
ggml-ci
* common : sanity check for non-NULL tokens
ggml-ci
* ci : adjust rank score interval
ggml-ci
* ci : add shebang to run.sh
ggml-ci
* Add scaffolding for ggml logging macros
* Metal backend now uses GGML logging
* Cuda backend now uses GGML logging
* Cann backend now uses GGML logging
* Add enum tag to parameters
* Use C memory allocation funcs
* Fix compile error
* Use GGML_LOG instead of GGML_PRINT
* Rename llama_state to llama_logger_state
* Prevent null format string
* Fix whitespace
* Remove log callbacks from ggml backends
* Remove cuda log statement
* feat(gguf-py): Add granitemoe architecture
This includes the addition of new tensor names for the new moe layers.
These may not be correct at this point due to the need for the hack in
gguf_writer.py to double-check the length of the shape for these layers.
Branch: GraniteMoE
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(convert_hf_to_gguf): Add GraniteMoeModel
GraniteMoe has the same configuration deltas as Granite
Branch: GraniteMoE
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(granitemoe convert): Split the double-sized input layer into gate and up
After a lot of staring and squinting, it's clear that the standard mixtral
expert implementation is equivalent to the vectorized parallel experts in
granite. The difference is that in granite, the w1 and w3 are concatenated
into a single tensor "input_linear." Rather than reimplementing all of the
math on the llama.cpp side, the much simpler route is to just split this
tensor during conversion and follow the standard mixtral route.
Branch: GraniteMoE
Co-Authored-By: alex.brooks@ibm.com
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(granitemoe): Implement granitemoe
GraniteMoE follows the mixtral architecture (once the input_linear layers
are split into gate_exps/up_exps). The main delta is the addition of the
same four multipliers used in Granite.
Branch: GraniteMoE
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* Typo fix in docstring
Co-Authored-By: ggerganov@gmail.com
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(conversion): Simplify tensor name mapping in conversion
Branch: GraniteMoE
Co-Authored-By: git@compilade.net
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(convert): Remove unused tensor name mappings
Branch: GraniteMoE
Co-Authored-By: git@compilade.net
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(convert): Sanity check on merged FFN tensor sizes
Branch: GraniteMoE
Co-Authored-By: git@compilade.net
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Allow "output" layer in granite moe architecture (convert and cpp)
Branch: GraniteMoE
Co-Authored-By: git@compilade.net
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(granite): Add missing 'output' tensor for Granite
This is a fix for the previous `granite` architecture PR. Recent snapshots
have included this (`lm_head.weights`) as part of the architecture
Branch: GraniteMoE
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit updates the llama_sampler_sample function to use reserve and
emplace_back for the vector of llama_token_data structs.
The motivation for this change is to avoid the creation of n_vocab
default-constructed llama_token_data structs which are then
immediately overwritten.
* llama: fixed n_vocab for `no_vocab` models
* llama: updated error output for `llama_decode_internal` and `llama_encode_internal`
* llama: log warning if there's no vocab_size in metadata
* llama: correct vocab size for logging
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat(gguf-py): Add Granite model and params to gguf-py
Branch: GraniteLM
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(convert_hf_to_gguf): Add registration and param setup for Granite
Branch: GraniteLM
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Add config parsing for Granite multiplier params
Branch: GraniteLM
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): First pass at full port of granite deviations from llama
Something is still not working right since the results are mostly terrible,
but on occasion it's producing relevant results at this point, so
_something_ is working.
Branch: GraniteLM
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama.cpp): Determine granite language 3b instruct by vocab size
Branch: GraniteLM
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(convert_hf_to_gguf): Use LlamaModel as base for GraniteModel
The defaults in LlamaModel are needed for Granite as well
Branch: GraniteLM
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama.cpp): Switch Granite param names to use _scale for consistency
Other scalar multipliers are called *_scale, so this provides a more
consistent naming convention.
Branch: GraniteLM
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(convert_hf_to_gguf/gguf-py): _multiplier -> _scale
The transformers names with _multiplier will now be converted to the _scale
equivalent during conversion.
Branch: GraniteLM
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama.cpp): Use separate switch clause for granite in llm_load_hparams
Branch: GraniteLM
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit renames n_embed to n_embd in llm_build_rwkv6_time_mix.
The motivation for this change is consistency with the other rwkv6
functions like build_rwkv6 (and other parts of the code base).
This commit makes the cell_id variable const in the inp_s_mask block.
The motivation for this change is consistency with the code in the
inp_s_copy block.
* llama : llama_perf + option to disable timings during decode
ggml-ci
* common : add llama_arg
* Update src/llama.cpp
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* perf : separate functions in the API
ggml-ci
* perf : safer pointer handling + naming update
ggml-ci
* minor : better local var name
* perf : abort on invalid sampler pointer
ggml-ci
---------
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
This commit updates the comment, which seems to contain a typo or be an
outdated comment, in the copy_mask_state function changing the variable
n_rs to n_kv.
I believe this change is correct and what the comment wants to
convey is to copy the states that are not going to be used in the
upcoming processing, which are the tokens states from n_seqs up to
the number of possible token states n_kv.
* common : do not add null tokens during warmup
ggml-ci
* llama : check that the input tokens are valid
ggml-ci
* tests : fix batch size of bert model
ggml-ci
- Add `struct llama_sampler` and `struct llama_sampler_i`
- Add `llama_sampler_` API
- Add `llama_sampler_chain_` API for chaining multiple samplers
- Remove `LLAMA_API_INTERNAL`
- Add `llama_perf_` API and remove old `llama_print_timings` and `llama_reset_timings`
* ggml-quants : 1.625 bpw ternary packing for BitNet 1.58b
* ggml-quants : faster 1.625 bpw AVX2 vec_dot
Not using a lookup table anymore makes it match q4_0 speed.
* gguf-py : fix formatting
* llama : remove spaces on empty line
* ggml-quants : subtract 1 when back in epi8
This makes the 1.625 bpw type go faster than q4_0. Still not the fastest.
* ggml-quants : Q2_2 now faster than Q4_K on with AVX2
* ggml-quants : cleanup Q1_3 code formatting
* ggml-quants : ARM NEON vec_dot for q2_2 and q1_3
* ggml-quants : use ceiling division when quantizing q1_3
* convert-hf : simplify BitNet pre-quantization
This still results in the exact same tensor weights and scales,
but it reveals some weirdness in the current algorithm.
* convert-hf : allow converting the weird BitNet 1.3B
Its FFN size is 5460 which is not convenient.
The offending tensors are kept in F16,
which makes the final model 5.01 bpw.
* bitnet : replace 1.58b with b1.58, as in the paper
* ggml-quants : fix build failure on Windows
* ggml-quants : attempt to fix Arm 32-bit support
* ggml : add some informative comments in q1_3 vec_dot
* ggml : add TQ1_0 and TQ2_0 ternary quantization types
* ggml : even faster TQ2_0
* ggml : also faster TQ1_0
Same optimization as for TQ2_0 by offsetting the sum instead of the weights.
This makes TQ1_0 almost as fast as Q8_0 on AVX2.
* ggml : fix build issues in certain environments
* ggml : add NEON vec_dot implementation for TQ1_0 and TQ2_0
* ggml : avoid directly using vmlal_high_s8, for 32-bit ARM compat
The compiler seems smart enough to use the same instruction
even when using vget_high_s8 instead.
* ggml : remove q1_3 and q2_2
No more 1.625 bpw and 2.000 bpw,
now instead using 1.6875 bpw and 2.0625 bpw
with TQ1_0 and TQ2_0, respectively.
* llama : remove the separate scale tensors of BitNet b1.58
They won't be needed, since the remaining ternary quant types have
built-in scales.
* ggml-quants : rename fields of TQ1_0 and TQ2_0 structs for consistency
* ggml-quants : allow using vdotq_s32 in TQ2_0 vec_dot
Not yet tested on hardware which supports it,
might not work or might not even compile. But also it might.
It should make the performance better on recent ARM CPUs.
* ggml-quants : remove comment about possible format change of TQ2_0
Making it slightly more convenient for AVX512
but less convenient for everything else is not worth the trouble.
* gguf-py : Numpy (de)quantization for TQ1_0 and TQ2_0
* ggml-quants : use roundf instead of nearest_int for TQ1_0 and TQ2_0
This does not change anything for ternary models,
since their values should never end up being in halfway cases anyway.
* convert : allow direct conversion to TQ1_0 and TQ2_0
The token embeddings and output tensors are kept in F16
to allow quantizing them to Q4_K and Q6_K with llama-quantize.
* llama : handle fallback for TQ1_0 and TQ2_0 with Q4_0
Q4_0 is not completely symmetric (so not lossless for ternary models),
but it should be good enough.
* ggml-quants : allow using ARM dot product instructions for TQ1_0
* ggml-quants : deduplicate TQ1_0 and TQ2_0 __ARM_FEATURE_DOTPROD support
* ggml : remove unused ggml_mul special case
It would otherwise conflict with the more general
optimization coming with Mamba-2.
* ggml : handle TQ1_0 and TQ2_0 in dequantization-based operators
* test-backend-ops : add TQ1_0 and TQ2_0 comments for later
Not yet adding uncommented, because some backends like SYCL and Metal
do not properly handle unknown types in supports_op for GGML_OP_MUL_MAT.
(and Metal also doesn't handle it with GGML_OP_GET_ROWS)
Support for TQ1_0 and TQ2_0 for other backends than CPU
will be added in follow-up pull requests.
* Introduce ggml_compute_threadpool
- OpenMP functional: check
- Vanilla ggml functional: Check
- ggml w/threadpool functional: Check
- OpenMP no regression: No glaring problems
- Vanilla ggml no regression: No glaring problems
- ggml w/threadpool no regression: No glaring problems
* Minor fixes
* fixed use after release bug
* fixed a harmless race condition
* Fix Android bulid issue
* fix more race conditions
* fix deadlock for cases where cgraph.n_nodes == 1
and fix --poll case
* threadpool: use cpu_get_num_math to set the default number of threadpool threads
This way we avoid using E-Cores and Hyperthreaded siblings.
* bench: create fresh threadpool for each test
For benchmarking it's better to start a fresh pool for each test with the exact number of threads
needed for that test. Having larger pools is suboptimal (causes more load, etc).
* atomics: always use stdatomics with clang and use relaxed memory order when polling in ggml_barrier
This also removes sched_yield() calls from ggml_barrier() to match OpenMP behavior.
* threadpool: make polling the default to match openmp behavior
All command line args now allow for setting poll to 0 (false).
* threadpool: do not wakeup threads in already paused threadpool
* fix potential race condition in check_for_work
* threadpool: do not create two threadpools if their params are identical
* threadpool: reduce pause/resume/wakeup overhead in common cases
We now start threadpool in paused state only if we have two.
The resume is now implicit (ie new work) which allows for reduced locking and context-switch overhead.
* threadpool: add support for hybrid polling
poll params (--poll, ...) now specify "polling level", i.e. how aggresively we poll before waiting on cond.var.
poll=0 means no polling, 1 means poll for 128K rounds then wait, 2 for 256K rounds, ...
The default value of 50 (ie 50x128K rounds) seems like a decent default across modern platforms.
We can tune this further as things evolve.
* threadpool: reduce the number of barrier required
New work is now indicated with an atomic counter that is incremented for
each new graph that needs to be computed.
This removes the need for extra barrier for clearing the "new_work" and
removes the special case for trivial graphs.
* threadpool: remove special-casing for disposable threadpools
With the efficient hybrid polling there is no need to make disposable pools any different.
This simplifies the overall logic and reduces branching.
Include n_threads in debug print for disposable threadpool.
Declare pause and stop flags as atomic_bool
This doesn't actually generate any memory barriers and simply informs
the thread sanitizer that these flags can be written & read by different
threads without locking.
* threadpool: do not clear barrier counters between graphs computes (fixes race with small graphs)
This fixes the race condition with very small graphs where the main thread happens to
start a new graph while the workers are just about to exit from barriers.
* threadpool: use relaxed order for chunk sync
Full memory barrier is an overkill for this since each thread works on different chunk
* threadpool: remove abort_callback from threadpool state
* threadpool: better naming for thread/cpumask releated functions
* threadpool: consistent use of int type for n_threads params
* threadpool: add support for ggml_threadpool_params_default/init
Also removes the need for explicit mask_specified param.
all-zero cpumask means use default (usually inherited) cpu affinity mask.
* threadpool: move typedef into ggml.h
* threadpool: fix apply_priority() function name
* threadpool: fix swift wrapper errors due to n_threads int type cleanup
* threadpool: enable --cpu-mask and other threadpool related options only if threadpool is enabled
* threadpool: replace checks for compute_thread ret code with proper status check
* threadpool: simplify threadpool init logic and fix main thread affinity application
Most of the init code is now exactly the same between threadpool and openmp.
* threadpool: update threadpool resume/pause function names
* threadpool: enable openmp by default for now
* threadpool: don't forget to free workers state when omp is enabled
* threadpool: avoid updating process priority on the platforms that do not require it
On Windows we need to change overall process priority class in order to set thread priorities,
but on Linux, Mac, etc we do not need to touch the overall process settings.
* threadpool: update calling thread prio and affinity only at start/resume
This avoids extra syscalls for each graph_compute()
* llama-bench: turn threadpool params into vectors, add output headers, etc
* llama-bench: add support for cool off between tests --delay
This helps for long running tests on platforms that are thermally limited (phones, laptops, etc).
--delay (disabled by default) introduces the sleep for N seconds before starting each test.
* threadpool: move process priority setting into the apps (bench and cli)
This avoids changing the overall process priority on Windows for the apps
that use ggml/llama.cpp directy.
* threadpool: move all pause/resume logic into ggml
* threadpool: futher api cleanup and prep for future refactoring
All threadpool related functions and structs use ggml_threadpool prefix.
* threadpool: minor indent fixes
* threadpool: improve setprioty error message
* Update examples/llama-bench/llama-bench.cpp
Co-authored-by: slaren <slarengh@gmail.com>
* threadpool: fix indent in set_threadpool call
* use int32_t for n_thread type in public llama.cpp API
* threadpool: use _new and _free instead of _create and _release
* fix two more public APIs to use int32_t for n_threads
* build: set _GNU_SOURCE for Adroid
---------
Co-authored-by: Max Krasnyansky <quic_maxk@quicinc.com>
Co-authored-by: fmz <quic_fzaghlou@quic.com>
Co-authored-by: Max Krasnyansky <max.krasnyansky@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
This change fixes a bug where replacing text in a very long string could
cause llama.cpp to hang indefinitely. This is because the algorithm used
was quadratic, due to memmove() when s.replace() is called in a loop. It
seems most search results and LLM responses actually provide the O(n**2)
algorithm, which is a great tragedy. Using a builder string fixes things
* llama : advanced batch splits
This includes equal-sequence-length batch splits which are useful
to simplify recurrent model operators.
* llama : always make recurrent state slots contiguous
* ggml : simplify mamba operators
* llama : fix integer signedness mixing
* llama : logits_all has priority over batch->logits
Otherwise, the server embeddings tests failed.
This was likely an existing problem but was only detected here
because of an additional assertion.
* llama : apply suggestions
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : fix t5 segfault
* llama : fix Mamba session save and restore
* llama : minor cosmetic changes
* llama : rename llama_reorder_outputs to llama_output_reorder
Also move it closer to llama_output_reserve.
* llama : fix pooled embeddings when using batches with equal_seqs
* minor : add struct members for clarity
ggml-ci
* llama : fix T5 segfault again
* llama : fix Mamba pooled embeddings with multiple sequences
Until the pooled embeddings are refactored to allow splitting
across ubatches for causal embeddings,
recurrent models can only process a single sequence per ubatch
when calculating pooled embeddings.
* llama : add llama_model_is_recurrent to simplify figuring that out
This will make it easier to more cleanly support RWKV-v6 and Mamba-2.
* llama : fix simple splits when the batch contains embeddings
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : std::move llm_bigram_bpe from work_queue
This commit updates the retrieval of llm_bigram_bpe objects from
work_queue.top() by using std::move.
The motivation for this is to avoid the copying of the std::string
`text` member of the llm_bigram_bpe struct.
* squash! llama : std::move llm_bigram_bpe from work_queue
Introduced a MovablePriorityQueue class to allow moving elements
out of the priority queue for llm_bigram_bpe.
* squash! llama : std::move llm_bigram_bpe from work_queue
Rename MovablePriorityQueue to lama_priority_queue.
* squash! llama : std::move llm_bigram_bpe from work_queue
Rename lama_priority_queue -> llama_priority_queue.
* gguf-py : add T5ENCODER model architecture
* common : call llama_decode() during warmup only if the model has decoder
* convert-hf : add T5EncoderModel
* llama : add llama_model_has_decoder() API function
* llama : split build_t5() into build_t5_encoder() and build_t5_decoder()
* llama : add support for LLM_ARCH_T5ENCODER
* llama-embedding : add support for LLAMA_POOLING_TYPE_NONE
* llama-embedding : add support for encoder-only models
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* gguf-py, llama : add constants and methods related to Llama-3.1 <|eom_id|> token
* llama : find Llama-3.1 <|eom_id|> token id during vocab loading
* llama-vocab : add Llama-3.1 <|eom_id|> token to the set of tokens stopping the generation
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* llama : refactor session file management
* llama : saving and restoring state checks for overflow
The size of the buffers should now be given to the functions working
with them, otherwise a truncated file could cause out of bound reads.
* llama : stream from session file instead of copying into a big buffer
Loading session files should no longer cause a memory usage spike.
* llama : llama_state_get_size returns the actual size instead of max
This is a breaking change, but makes that function *much* easier
to keep up to date, and it also makes it reflect the behavior
of llama_state_seq_get_size.
* llama : share code between whole and seq_id-specific state saving
Both session file types now use a more similar format.
* llama : no longer store all hparams in session files
Instead, the model arch name is stored.
The layer count and the embedding dimensions of the KV cache
are still verified when loading.
Storing all the hparams is not necessary.
* llama : fix uint64_t format type
* llama : various integer type cast and format string fixes
Some platforms use "%lu" and others "%llu" for uint64_t.
Not sure how to handle that, so casting to size_t when displaying errors.
* llama : remove _context suffix for llama_data_context
* llama : fix session file loading
llama_state_get_size cannot be used to get the max size anymore.
* llama : more graceful error handling of invalid session files
* llama : remove LLAMA_MAX_RNG_STATE
It's no longer necessary to limit the size of the RNG state,
because the max size of session files is not estimated anymore.
* llama : cast seq_id in comparison with unsigned n_seq_max
* Add llama 3.1 rope scaling factors to llama conversion and inference
This commit generates the rope factors on conversion and adds them to the resulting model as a tensor. At inference time, these factors are passed to the `ggml_rope_ext` rope oepration, improving results for context windows above 8192
* Update convert_hf_to_gguf.py
Co-authored-by: compilade <git@compilade.net>
* address comments
* address comments
* Update src/llama.cpp
Co-authored-by: compilade <git@compilade.net>
* Update convert_hf_to_gguf.py
Co-authored-by: compilade <git@compilade.net>
---------
Co-authored-by: compilade <git@compilade.net>
oobabooga
ymcki
Georgi Gerganov
Jared Van Bortel
Sigbjørn Skjæret
piDack
matteo
ddh0
Johannes Gäßler
Xuan-Son Nguyen
AT
pockers21
Diego Devesa
City
Mikko Juola
Juk Armstrong
Ed Addario
Yuxuan Zhang
Bo Zheng
Plamen Minev
yumeyao
jklincn
Daniel Bevenius
Juyoung Suk
Sergei Vorobyov
Djip007
Si1w
HighDoping
compilade
Prajwal B Mehendarkar
fairydreaming
Molly Sophia
Olivier Chafik
mgroeber9110
Xuan-Son Nguyen
Vitali Lovich
Vinesh Janarthanan
bandoti
Wilken Gottwalt
Christian Fillion
magicse
tv1wnd
lexasub
Frank Mai
Christopher Nielsen
Kyle Bruene
Radoslav Gerganov
Pierrick Hymbert
Asghar Ghorbani
DAN™
Yun Dou
Billel Mokeddem
Sukriti Sharma
Valentin Mamedov
HimariO
Riccardo Orlando
JFLFY2255
Shane A
FirstTimeEZ
Charles Xu
Michael Podvitskiy
wwoodsTM
Zhiyuan Li
Gabe Goodhart
Zhenwei Jin
arch-btw
leo-pony
Ouadie EL FAROUKI
Ma Mingfei
MaggotHATE
nopperl
Ivan
Shankar
Yuri Khrustalev
CarryFun
Gilad S.
Dou Xinpeng
Ahmad Tameem
Kevin Gibbons
Faisal Zaghloul
CausalLM
Carsten Kragelund Jørgensen
Justine Tunney
Younes Belkada
Yoshi Suhara
Minsoo Cheong
Yoshi Suhara
Esko Toivonen
Nico Bosshard
Liu Jia
Douglas Hanley
pculliton
Jeffrey Morgan